Abstract
Due to the complexity and dangerousness of drying process, the fault detection of temperature sensor is very difficult and dangerous in actual working practice and the detection effectiveness is not satisfying. For this problem, in this paper, based on the idea of information fusion and the requirements of D-S evidence method, a D-S evidence fusion structure with two layers was introduced to detect the temperature sensor fault in drying process. The first layer was data layer to establish the basic belief assignment function of evidence which could be realized by BP Neural Network. The second layer was decision layer to detect and locate the sensor fault which could be realized by D-S evidence fusion method. According to the numerical simulation results, the working conditions of sensors could be described effectively and accurately by this method, so that it could be used to detect and locate the sensor fault.
Highlights
Information fusion is a useful technique to integrate heterogeneous data from different information sources
Suppose m1 and m2 are two mass functions formed based on the information from two different information sources in the same frame of discernment θ and that Dempster’s rule of combination, noted by m = m1 ⊕m2, is the first one within the framework of evidence theory which can combine two basic probability assignment (BPA) m1 and m2 to yield a new BPA: m (A) = ∑B∩C=A m1 (B) ⋅ m2 (C), 1−k (8)
A modular and generic framework for multiple fault detection and isolation of sensors was presented with a two-layer structure
Summary
Information fusion is a useful technique to integrate heterogeneous data from different information sources. According to the fusion result, the working conditions of sensor could be described effectively and accurately In this fusion process, Mathematical Problems in Engineering on the one hand, the BP Neural Network could provide the ability of self-learning, self-adaptation, and fault tolerance; on the other hand, the D-S evidence method could express and handle the uncertain, incomplete, and imprecise information. Mathematical Problems in Engineering on the one hand, the BP Neural Network could provide the ability of self-learning, self-adaptation, and fault tolerance; on the other hand, the D-S evidence method could express and handle the uncertain, incomplete, and imprecise information This method could further improve the accuracy and robustness of sensor monitoring system, which was proved by the numerical simulation results
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